Data Science | Machine Learning with Python for Researchers
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The Data Science and Python channel is for researchers and advanced programmers

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[NeurIPS 2023] Global Structure-Aware Diffusion Process for Low-Light Image Enhancement


🖥 Github: https://github.com/jinnh/GSAD

📕 Paper: https://arxiv.org/pdf/2310.17577.pdf

🔥 Datasets: https://paperswithcode.com/dataset/lol
CS25: Transformers United V3

New lectures on the course on Transformers from Stanford! Stanford CS 25 " Transformers United " featured celebrity guests such as Andriy Karpaty, Noam Brown, Lukas Beyer and Geoff Hinton himself!

A new report has been released on the creation and recipes for creating universal AI agents in open worlds:

🟢 MineDojo : an open framework and multimodal database for training Minecraft agents.

🟢 Voyager : agent for lifelong learning in Minecraft based on LLM.

🟢 Eureka: GPT-4 develops reward functions to teach a robot hand to turn a knob.

🟢 VIMA : one of the earliest multimodal LLMs.

🟢A look into the future: promising areas of research.

☑️ Slides : https://drive.google.com/file/d/1lWIhijUaTZkkWOC_YwZHMoI0h7EAWVPL/view

📑 Lectures : https://web.stanford.edu/class/cs25

👌 https://t.iss.one/CodeProgrammer

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⚡️ LLMRec: Large Language Models with Graph Augmentation for Recommendation

🖥 Github: https://github.com/hkuds/llmrec

📕 Paper: https://arxiv.org/abs/2311.00423v1

Project: https://llmrec.github.io/

🌐 Dataset: https://llmrec.github.io/#

https://t.iss.one/DataScienceT
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🖥 TORCH UNCERTAINTY

Comprehensive PyTorch Library for deep learning uncertainty quantification techniques.

pip install torch-uncertainty

🖥 Github: https://github.com/ensta-u2is/torch-uncertainty

📕 Paper: https://arxiv.org/abs/2311.01434v1

Project: https://llmrec.github.io/

👣 Api: https://torch-uncertainty.github.io/api.html

🌐 Dataset: https://paperswithcode.com/dataset/cifar-10

https://t.iss.one/DataScienceT
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PETA: Evaluating the Impact of Protein Transfer Learning with Sub-word Tokenization on Downstream Applications

🖥 Github: https://github.com/ginnm/proteinpretraining

📕 Paper: https://arxiv.org/pdf/2310.17415v1.pdf

🔥 Datasets: https://paperswithcode.com/dataset/peta-protein

Tasks: https://paperswithcode.com/task/language-modelling

https://t.iss.one/DataScienceT
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🎧 Video2Music: Suitable Music Generation from Videos using an Affective Multimodal Transformer model

Video2Music: Suitable Music Generation from Videos using an Affective Multimodal Transformer model.

🖥 Github: https://github.com/amaai-lab/video2music

📕 Paper: https://arxiv.org/abs/2311.00968v1

Demo: https://llmrec.github.io/

🌐 Dataset: https://zenodo.org/records/10057093

https://t.iss.one/DataScienceT
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Top execs from billion-dollar giants are whispering about next crypto "GEM". Want in?

This Tuesday, Blockchain Whispers pulls back the curtain. Join the insiders now: https://t.iss.one/+c5yEZuGFtsc5NDlk
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Bilingual Corpus Mining and Multistage Fine-Tuning for Improving Machine Translation of Lecture Transcripts

🖥 Github: https://github.com/shyyhs/CourseraParallelCorpusMining

📕 Paper: https://arxiv.org/abs/2311.03696v1

🔥 Datasets: https://paperswithcode.com/dataset/aspec

https://t.iss.one/DataScienceT
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Large Language Models (in 2023)

An excellent summary of the research progress and developments in LLMs.

Hyung Won chung, OpenAI (ex.Google and MIT Alumni) made this content publicly available. It's a great way to catch up on some important themes like scaling and optimizing LLMs.

Watch his talk here and Slides shared here.

https://t.iss.one/DataScienceT
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🚀 Whisper-V3 / Consistency Decoder

Improved decoding for stable diffusion vaes.

- Whisper paper: https://arxiv.org/abs/2212.04356
- Whisper-V3 checkpoint: https://github.com/openai/whisper/discussions/1762
- Consistency Models: https://arxiv.org/abs/2303.01469
- Consistency Decoder release: https://github.com/openai/consistencydecoder

https://t.iss.one/DataScienceT
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NVIDIA just made Pandas 150x faster with zero code changes.

All you have to do is:
%load_ext cudf.pandas
import pandas as pd


Their RAPIDS library will automatically know if you're running on GPU or CPU and speed up your processing.

You can try it in this colab notebook

GitHub repo: https://github.com/rapidsai/cudf

https://t.iss.one/DataScienceT
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